GazeTrack: High-Precision Eye Tracking Based on Regularization and Spatial Computing

TL;DR

This paper introduces GazeTrack, a high-precision eye tracking framework that leverages a new dataset, regularization techniques, and coordinate transformations to improve gaze accuracy for spatial computing applications.

Contribution

The paper presents a novel dataset, a shape error regularization method, and a coordinate transformation approach, advancing the accuracy and efficiency of eye tracking in virtual and augmented reality.

Findings

GazeTrack dataset includes diverse demographics and conditions.

The proposed methods improve gaze prediction accuracy.

Reduced computational complexity compared to existing approaches.

Abstract

Eye tracking has become increasingly important in virtual and augmented reality applications; however, the current gaze accuracy falls short of meeting the requirements for spatial computing. We designed a gaze collection framework and utilized high-precision equipment to gather the first precise benchmark dataset, GazeTrack, encompassing diverse ethnicities, ages, and visual acuity conditions for pupil localization and gaze tracking. We propose a novel shape error regularization method to constrain pupil ellipse fitting and train on open-source datasets, enhancing semantic segmentation and pupil position prediction accuracy. Additionally, we invent a novel coordinate transformation method similar to paper unfolding to accurately predict gaze vectors on the GazeTrack dataset. Finally, we built a gaze vector generation model that achieves reduced gaze angle error with lower computational complexity compared to other methods.